longevity-db/snRNAseq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells

Human Optic Nerve Endothelial Cells (snRNA-seq) Dataset

Dataset Overview

This dataset comprises single-nucleus RNA sequencing (snRNA-seq) data specifically focusing on endothelial cells from the human optic nerve and optic nerve head. It represents a valuable resource for investigating cell-type specific gene expression profiles within a critical ocular tissue.

The data was sourced from the CZ CELLxGENE Discover API, providing access to a deeply characterized single-cell atlas. It has been processed and converted into standardized .h5ad and .parquet formats for ease of use in machine learning and bioinformatics pipelines, enabling high-resolution insights into tissue biology.

Relevance to Aging Research

The human optic nerve and optic nerve head are highly susceptible to age-related degeneration, playing a critical role in conditions like glaucoma, a leading cause of irreversible blindness, which is strongly associated with aging. Endothelial cells, forming the lining of blood vessels, are crucial for maintaining tissue homeostasis, nutrient supply, and waste removal in the optic nerve.

Age-related changes in endothelial cell function, integrity, and gene expression are known to contribute to vascular dysfunction, inflammation, and cellular senescence, all of which are hallmarks of aging. By providing a single-cell resolution view of these specific cells, this dataset offers an unprecedented opportunity to:

• Identify age-specific molecular signatures within optic nerve endothelial cells.
• Uncover how cellular processes like metabolism, inflammation, and stress response change with age at the single-cell level.
• Discover biomarkers or therapeutic targets related to age-associated optic neuropathies.
• Investigate the contribution of endothelial cell aging to the overall aging process of the visual system.

This dataset thus serves as a powerful resource for understanding the intricate molecular mechanisms of aging within a vital human tissue.

Data Details

• Organism: Homo sapiens (Human)
• Tissue: Optic nerve, Optic nerve head, Optic disc, Cranial nerve II
• Cell Type: Endothelial cells
• Technology: 10x Genomics 3' v3 snRNA-seq
• Condition: Normal
• Number of Cells: 34,934
• Associated CELLxGENE Collection ID: 05e3d0fc-c9dd-4f14-9163-2b242b3bb5c2
• Associated CELLxGENE Dataset ID: f5b09167-e4b5-4f32-b7b4-f0b7c402a4c4

Dataset Structure

The dataset is provided in formats commonly used in single-cell genomics and tabular data analysis:

• snRNA_seq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells.h5ad: The primary AnnData object, encapsulating the gene expression matrix, cell metadata, and gene metadata. This is the most comprehensive format for single-cell analysis using tools like Scanpy.
• expression.parquet: A tabular representation of the gene expression matrix (corresponding to adata.X), where rows are cells and columns are genes. Ideal for direct use in machine learning models that expect a feature matrix.
• feature_metadata.parquet: A tabular representation of the gene (feature) metadata (corresponding to adata.var), providing details about each gene such as gene symbols.

Data Cleaning and Processing

The raw data was accessed and retrieved via the cellxgene_census API, which ensures standardized data types and preliminary quality control as part of the Census pipeline. Further processing involved:

• Extraction of the relevant cell and gene metadata, ensuring appropriate data types for storage in adata.obs and adata.var.
• Conversion of the TileDB-SOMA object (from Census) into a standard AnnData object (.h5ad).
• Exporting the main expression matrix and gene metadata into Parquet format for broader compatibility with tabular data tools and machine learning frameworks.

The .h5ad file specifically retains rich metadata from the Census, including organism, tissue, cell type annotations, and more, which can be leveraged for detailed analysis.

Usage

This dataset is ideal for a variety of research and machine learning tasks, including:

• Single-Cell Analysis: Exploring cellular heterogeneity, identifying novel cell states, and characterizing gene expression patterns in the optic nerve's endothelial cells.
• Aging Research: Investigating age-related changes in endothelial cell gene expression and function within the optic nerve, identifying biomarkers or therapeutic targets for age-related ocular diseases.
• Machine Learning: Developing models for cell type classification, dimensionality reduction, feature selection, and identifying gene signatures associated with specific biological states or aging phenotypes.
• Biomarker Discovery: Pinpointing genes or pathways implicated in optic nerve health and age-related visual impairment.

Direct Download and Loading from Hugging Face Hub

This dataset is hosted on the Hugging Face Hub, allowing for easy programmatic download and loading of its component files.

import pandas as pd
from huggingface_hub import hf_hub_download
import os
import anndata as ad

# Define the Hugging Face repository ID and the local directory for downloads
HF_REPO_ID = "longevity-db/snRNAseq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells"
LOCAL_DATA_DIR = "downloaded_optic_nerve_endothelial_data" # Suggest a distinct name for this specific dataset

os.makedirs(LOCAL_DATA_DIR, exist_ok=True)
print(f"Created local download directory: {LOCAL_DATA_DIR}")

# List of files to download
data_files = [
    "snRNA_seq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells.h5ad",
    "expression.parquet",
    "feature_metadata.parquet"
]

# Download each file
downloaded_paths = {}
for file_name in data_files:
    path = hf_hub_download(repo_id=HF_REPO_ID, filename=file_name, local_dir=LOCAL_DATA_DIR)
    downloaded_paths[file_name] = path
    print(f"Downloaded {file_name} to: {path}")

# Load the H5AD file into an AnnData object
adata = ad.read_h5ad(downloaded_paths["snRNA_seq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells.h5ad"])

# Load Parquet files into Pandas DataFrames
df_expression = pd.read_parquet(downloaded_paths["expression.parquet"])
df_feature_metadata = pd.read_parquet(downloaded_paths["feature_metadata.parquet"])

print("\n--- Data Loaded from Hugging Face Hub ---")
print(f"AnnData object shape: {adata.shape} (Cells x Genes)")
print("Expression data shape:", df_expression.shape)
print("Feature metadata shape:", df_feature_metadata.shape)

# Accessing metadata from AnnData (e.g., for 'age' or 'cell_type' if available in original data)
# Note: The presence and naming of columns like 'age' depend on the original CELLxGENE dataset's metadata.
print("\nExample Cell Metadata Columns (from AnnData.obs):")
print(adata.obs.columns)

# Example: If 'age' column exists in adata.obs (or a similar age-related column)
if 'age' in adata.obs.columns: # Replace 'age' with the actual column name if different
    X_features = adata.X # Use expression matrix or derived features (e.g., PCA from adata.obsm)
    y_labels = adata.obs['age'] # Target variable (e.g., age of cell's donor)
    print(f"\nPrepared X (features) with shape {X_features.shape} and y (labels) with shape {y_labels.shape} for a potential age prediction model.")
else:
    print("\nNo 'age' column explicitly named 'age' found in AnnData.obs for age prediction example.")
    print("Please inspect 'adata.obs.columns' to find relevant age-related or other label columns (e.g., 'cell_type').")

# This data can then be used for various ML tasks.

Download Instructions

You can download the files directly using the links below or with wget/curl.

File: snRNA_seq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells.h5ad

• Direct Link: Download .h5ad

• Using wget:

  wget https://huggingface.co/datasets/longevity-db/snRNAseq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells/resolve/main/snRNA_seq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells.h5ad
  

File: expression.parquet

• Direct Link: Download expression.parquet

• Using wget:

  wget https://huggingface.co/datasets/longevity-db/snRNAseq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells/resolve/main/expression.parquet
  

File: feature_metadata.parquet

• Direct Link: Download feature_metadata.parquet

• Using wget:

  wget https://huggingface.co/datasets/longevity-db/snRNAseq_of_human_optic_nerve_and_optic_nerve_head_endothelial_cells/resolve/main/feature_metadata.parquet
  

Citation

Please ensure you cite the original source of the data from CELLxGENE Discover. The specific publication for this dataset (GSE67547) can be found on its NCBI GEO page:

NCBI GEO Accession: GSE67547

When using the cellxgene_census API, you may also consider citing the Census project: CELLxGENE Census

The raw data can be downloaded from https://datasets.cellxgene.cziscience.com/f5b09167-e4b5-4f32-b7b4-f0b7c402a4c4.h5ad

Contact

For questions or feedback regarding this processed dataset, please contact:

Venkatachalam, Pooja, Albert

Curated by cellVPA on June 15, 2025

---